mirror of https://github.com/Mai-with-u/MaiBot.git
feat：记忆系统重出江湖，移除了即时记忆和定期记忆
SengokuCola
parent
01197cb2b7
commit
6d3e9fd3d4
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@ -1,10 +1,24 @@
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# Changelog
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TODO：回复频率动态控制
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## [0.10.2] - 2025-8-24
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### 🌟 主要功能更改
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- 记忆系统重新启用，更好更优秀
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- 更好的event系统
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- 为空回复添加重试机制
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### 细节功能更改
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- 修复tts插件可能的复读问题
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## [0.10.1] - 2025-8-24
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### 🌟 主要功能更改
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- planner现在改为大小核结构，移除激活阶段，提高回复速度和动作调用精准度
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- 优化关系的表现的效率
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### 细节功能更改
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- 优化识图的表现
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- 为planner添加单独控制的提示词
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- 修复激活值计算异常的BUG
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@ -385,12 +385,6 @@ class HeartFChatting:
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async with global_prompt_manager.async_message_scope(self.chat_stream.context.get_template_name()):
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await self.expression_learner.trigger_learning_for_chat()
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# # 记忆构建：为当前chat_id构建记忆
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# try:
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# await hippocampus_manager.build_memory_for_chat(self.stream_id)
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# except Exception as e:
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# logger.error(f"{self.log_prefix} 记忆构建失败: {e}")
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available_actions: Dict[str, ActionInfo] = {}
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if random.random() > self.focus_value_control.get_current_focus_value() and mode == ChatMode.FOCUS:
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# 如果激活度没有激活，并且聊天活跃度低，有可能不进行plan，相当于不在电脑前，不进行认真思考
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@ -445,8 +439,8 @@ class HeartFChatting:
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available_actions=available_actions,
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)
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for action in action_to_use_info:
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print(action.action_type)
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# for action in action_to_use_info:
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# print(action.action_type)
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# 3. 并行执行所有动作
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action_tasks = [
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@ -18,6 +18,7 @@ from src.config.config import global_config, model_config
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from src.common.data_models.database_data_model import DatabaseMessages
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from src.common.database.database_model import GraphNodes, GraphEdges # Peewee Models导入
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from src.common.logger import get_logger
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from src.chat.utils.utils import cut_key_words
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from src.chat.utils.chat_message_builder import (
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build_readable_messages,
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get_raw_msg_by_timestamp_with_chat_inclusive,
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@ -98,19 +99,23 @@ class MemoryGraph:
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current_weight = self.G.nodes[concept].get("weight", 0.0)
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self.G.nodes[concept]["weight"] = current_weight + 1.0
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logger.debug(f"节点 {concept} 记忆整合成功，权重增加到 {current_weight + 1.0}")
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logger.info(f"节点 {concept} 记忆内容已更新：{integrated_memory}")
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except Exception as e:
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logger.error(f"LLM整合记忆失败: {e}")
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# 降级到简单连接
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new_memory_str = f"{existing_memory} | {memory}"
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self.G.nodes[concept]["memory_items"] = new_memory_str
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logger.info(f"节点 {concept} 记忆内容已简单拼接并更新：{new_memory_str}")
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else:
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new_memory_str = str(memory)
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self.G.nodes[concept]["memory_items"] = new_memory_str
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logger.info(f"节点 {concept} 记忆内容已直接更新：{new_memory_str}")
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else:
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self.G.nodes[concept]["memory_items"] = str(memory)
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# 如果节点存在但没有memory_items,说明是第一次添加memory,设置created_time
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if "created_time" not in self.G.nodes[concept]:
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self.G.nodes[concept]["created_time"] = current_time
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logger.info(f"节点 {concept} 创建新记忆：{str(memory)}")
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# 更新最后修改时间
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self.G.nodes[concept]["last_modified"] = current_time
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else:
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@ -122,6 +127,7 @@ class MemoryGraph:
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created_time=current_time, # 添加创建时间
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last_modified=current_time,
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) # 添加最后修改时间
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logger.info(f"新节点 {concept} 已添加，记忆内容已写入：{str(memory)}")
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def get_dot(self, concept):
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# 检查节点是否存在于图中
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@ -402,9 +408,7 @@ class Hippocampus:
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text_length = len(text)
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topic_num: int | list[int] = 0
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words = jieba.cut(text)
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keywords_lite = [word for word in words if len(word) > 1]
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keywords_lite = list(set(keywords_lite))
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keywords_lite = cut_key_words(text)
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if keywords_lite:
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logger.debug(f"提取关键词极简版: {keywords_lite}")
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@ -1159,6 +1163,131 @@ class ParahippocampalGyrus:
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return compressed_memory, similar_topics_dict
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def get_similar_topics_from_keywords(
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self,
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keywords: list[str] | str,
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top_k: int = 3,
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threshold: float = 0.7,
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) -> dict[str, list[tuple[str, float]]]:
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"""基于输入的关键词，返回每个关键词对应的相似主题列表。
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Args:
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keywords: 关键词列表或以逗号/空格/顿号分隔的字符串。
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top_k: 每个关键词返回的相似主题数量上限。
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threshold: 相似度阈值，低于该值的主题将被过滤。
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Returns:
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dict[str, list[tuple[str, float]]]: {keyword: [(topic, similarity), ...]}
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"""
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# 规范化输入为列表[str]
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if isinstance(keywords, str):
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# 支持中英文逗号、顿号、空格分隔
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parts = (
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keywords.replace("，", ",").replace("、", ",").replace(" ", ",").strip(", ")
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)
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keyword_list = [p.strip() for p in parts.split(",") if p.strip()]
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else:
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keyword_list = [k.strip() for k in keywords if isinstance(k, str) and k.strip()]
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if not keyword_list:
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return {}
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existing_topics = list(self.memory_graph.G.nodes())
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result: dict[str, list[tuple[str, float]]] = {}
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for kw in keyword_list:
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kw_words = set(jieba.cut(kw))
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similar_topics: list[tuple[str, float]] = []
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for topic in existing_topics:
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topic_words = set(jieba.cut(topic))
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all_words = kw_words | topic_words
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if not all_words:
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continue
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v1 = [1 if w in kw_words else 0 for w in all_words]
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v2 = [1 if w in topic_words else 0 for w in all_words]
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sim = cosine_similarity(v1, v2)
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if sim >= threshold:
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similar_topics.append((topic, sim))
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similar_topics.sort(key=lambda x: x[1], reverse=True)
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result[kw] = similar_topics[:top_k]
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return result
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async def add_memory_with_similar(
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self,
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memory_item: str,
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similar_topics_dict: dict[str, list[tuple[str, float]]],
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) -> bool:
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"""将单条记忆内容与相似主题写入记忆网络并同步数据库。
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按 build_memory_for_chat 的方式：为 similar_topics_dict 的每个键作为主题添加节点内容，
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并与其相似主题建立连接，连接强度为 int(similarity * 10)。
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Args:
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memory_item: 记忆内容字符串，将作为每个主题节点的 memory_items。
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similar_topics_dict: {topic: [(similar_topic, similarity), ...]}
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Returns:
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bool: 是否成功执行添加与同步。
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"""
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try:
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if not memory_item or not isinstance(memory_item, str):
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return False
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if not similar_topics_dict or not isinstance(similar_topics_dict, dict):
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return False
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current_time = time.time()
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# 为每个主题写入节点
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for topic, similar_list in similar_topics_dict.items():
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if not topic or not isinstance(topic, str):
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continue
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await self.hippocampus.memory_graph.add_dot(topic, memory_item, self.hippocampus)
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# 连接相似主题
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if isinstance(similar_list, list):
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for item in similar_list:
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try:
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similar_topic, similarity = item
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except Exception:
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continue
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if not isinstance(similar_topic, str):
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continue
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if topic == similar_topic:
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continue
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# 强度按 build_memory_for_chat 的规则
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strength = int(max(0.0, float(similarity)) * 10) if similarity is not None else 0
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if strength <= 0:
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continue
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# 确保相似主题节点存在（如果没有，也可以只建立边，networkx会创建节点，但需初始化属性）
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if similar_topic not in self.memory_graph.G:
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# 创建一个空的相似主题节点，避免悬空边，memory_items 为空字符串
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self.memory_graph.G.add_node(
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similar_topic,
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memory_items="",
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weight=1.0,
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created_time=current_time,
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last_modified=current_time,
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)
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self.memory_graph.G.add_edge(
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topic,
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similar_topic,
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strength=strength,
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created_time=current_time,
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last_modified=current_time,
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)
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# 同步数据库
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await self.hippocampus.entorhinal_cortex.sync_memory_to_db()
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return True
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except Exception as e:
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logger.error(f"添加记忆节点失败: {e}")
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return False
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async def operation_forget_topic(self, percentage=0.005):
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start_time = time.time()
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logger.info("[遗忘] 开始检查数据库...")
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@ -1325,7 +1454,6 @@ class HippocampusManager:
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logger.info(f"""
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--------------------------------
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记忆系统参数配置:
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构建频率: {global_config.memory.memory_build_frequency}秒|压缩率: {global_config.memory.memory_compress_rate}
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遗忘间隔: {global_config.memory.forget_memory_interval}秒|遗忘比例: {global_config.memory.memory_forget_percentage}|遗忘: {global_config.memory.memory_forget_time}小时之后
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记忆图统计信息: 节点数量: {node_count}, 连接数量: {edge_count}
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--------------------------------""") # noqa: E501
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@ -1343,61 +1471,6 @@ class HippocampusManager:
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raise RuntimeError("HippocampusManager 尚未初始化，请先调用 initialize 方法")
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return await self._hippocampus.parahippocampal_gyrus.operation_forget_topic(percentage)
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async def build_memory_for_chat(self, chat_id: str):
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"""为指定chat_id构建记忆（在heartFC_chat.py中调用）"""
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if not self._initialized:
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raise RuntimeError("HippocampusManager 尚未初始化，请先调用 initialize 方法")
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try:
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# 检查是否需要构建记忆
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logger.info(f"为 {chat_id} 构建记忆")
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if memory_segment_manager.check_and_build_memory_for_chat(chat_id):
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logger.info(f"为 {chat_id} 构建记忆，需要构建记忆")
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messages = memory_segment_manager.get_messages_for_memory_build(chat_id, 50)
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build_probability = 0.3 * global_config.memory.memory_build_frequency
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if messages and random.random() < build_probability:
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logger.info(f"为 {chat_id} 构建记忆，消息数量: {len(messages)}")
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# 调用记忆压缩和构建
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(
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compressed_memory,
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similar_topics_dict,
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) = await self._hippocampus.parahippocampal_gyrus.memory_compress(
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messages, global_config.memory.memory_compress_rate
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)
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# 添加记忆节点
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current_time = time.time()
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for topic, memory in compressed_memory:
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await self._hippocampus.memory_graph.add_dot(topic, memory, self._hippocampus)
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# 连接相似主题
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if topic in similar_topics_dict:
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similar_topics = similar_topics_dict[topic]
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for similar_topic, similarity in similar_topics:
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if topic != similar_topic:
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strength = int(similarity * 10)
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self._hippocampus.memory_graph.G.add_edge(
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topic,
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similar_topic,
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strength=strength,
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created_time=current_time,
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last_modified=current_time,
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)
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# 同步到数据库
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await self._hippocampus.entorhinal_cortex.sync_memory_to_db()
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logger.info(f"为 {chat_id} 构建记忆完成")
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return True
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except Exception as e:
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logger.error(f"为 {chat_id} 构建记忆失败: {e}")
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return False
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return False
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async def get_memory_from_topic(
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self, valid_keywords: list[str], max_memory_num: int = 3, max_memory_length: int = 2, max_depth: int = 3
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) -> list:
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@ -1441,89 +1514,3 @@ class HippocampusManager:
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# 创建全局实例
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hippocampus_manager = HippocampusManager()
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# 在Hippocampus类中添加新的记忆构建管理器
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class MemoryBuilder:
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"""记忆构建器
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为每个chat_id维护消息缓存和触发机制，类似ExpressionLearner
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"""
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def __init__(self, chat_id: str):
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self.chat_id = chat_id
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self.last_update_time: float = time.time()
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self.last_processed_time: float = 0.0
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def should_trigger_memory_build(self) -> bool:
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# sourcery skip: assign-if-exp, boolean-if-exp-identity, reintroduce-else
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"""检查是否应该触发记忆构建"""
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current_time = time.time()
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# 检查时间间隔
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time_diff = current_time - self.last_update_time
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if time_diff < 600 / global_config.memory.memory_build_frequency:
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return False
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# 检查消息数量
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recent_messages = get_raw_msg_by_timestamp_with_chat_inclusive(
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chat_id=self.chat_id,
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timestamp_start=self.last_update_time,
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timestamp_end=current_time,
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)
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logger.info(f"最近消息数量: {len(recent_messages)}，间隔时间: {time_diff}")
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if not recent_messages or len(recent_messages) < 30 / global_config.memory.memory_build_frequency:
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return False
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return True
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def get_messages_for_memory_build(self, threshold: int = 25) -> List[DatabaseMessages]:
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"""获取用于记忆构建的消息"""
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current_time = time.time()
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messages = get_raw_msg_by_timestamp_with_chat_inclusive(
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chat_id=self.chat_id,
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timestamp_start=self.last_update_time,
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timestamp_end=current_time,
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limit=threshold,
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)
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if messages:
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# 更新最后处理时间
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self.last_processed_time = current_time
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self.last_update_time = current_time
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return messages or []
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class MemorySegmentManager:
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"""记忆段管理器
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管理所有chat_id的MemoryBuilder实例，自动检查和触发记忆构建
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"""
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def __init__(self):
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self.builders: Dict[str, MemoryBuilder] = {}
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def get_or_create_builder(self, chat_id: str) -> MemoryBuilder:
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"""获取或创建指定chat_id的MemoryBuilder"""
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if chat_id not in self.builders:
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self.builders[chat_id] = MemoryBuilder(chat_id)
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return self.builders[chat_id]
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def check_and_build_memory_for_chat(self, chat_id: str) -> bool:
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"""检查指定chat_id是否需要构建记忆，如果需要则返回True"""
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builder = self.get_or_create_builder(chat_id)
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return builder.should_trigger_memory_build()
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def get_messages_for_memory_build(self, chat_id: str, threshold: int = 25) -> List[DatabaseMessages]:
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"""获取指定chat_id用于记忆构建的消息"""
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if chat_id not in self.builders:
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return []
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return self.builders[chat_id].get_messages_for_memory_build(threshold)
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# 创建全局实例
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memory_segment_manager = MemorySegmentManager()
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@ -1,254 +0,0 @@
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# -*- coding: utf-8 -*-
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import time
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import re
|
||||
import json
|
||||
import ast
|
||||
import traceback
|
||||
|
||||
from json_repair import repair_json
|
||||
from datetime import datetime, timedelta
|
||||
|
||||
from src.llm_models.utils_model import LLMRequest
|
||||
from src.common.logger import get_logger
|
||||
from src.common.database.database_model import Memory # Peewee Models导入
|
||||
from src.config.config import model_config, global_config
|
||||
|
||||
|
||||
logger = get_logger(__name__)
|
||||
|
||||
|
||||
class MemoryItem:
|
||||
def __init__(self, memory_id: str, chat_id: str, memory_text: str, keywords: list[str]):
|
||||
self.memory_id = memory_id
|
||||
self.chat_id = chat_id
|
||||
self.memory_text: str = memory_text
|
||||
self.keywords: list[str] = keywords
|
||||
self.create_time: float = time.time()
|
||||
self.last_view_time: float = time.time()
|
||||
|
||||
|
||||
class MemoryManager:
|
||||
def __init__(self):
|
||||
# self.memory_items:list[MemoryItem] = []
|
||||
pass
|
||||
|
||||
|
||||
class InstantMemory:
|
||||
def __init__(self, chat_id):
|
||||
self.chat_id = chat_id
|
||||
self.last_view_time = time.time()
|
||||
self.summary_model = LLMRequest(
|
||||
model_set=model_config.model_task_config.utils,
|
||||
request_type="memory.summary",
|
||||
)
|
||||
|
||||
async def if_need_build(self, text: str):
|
||||
prompt = f"""
|
||||
请判断以下内容中是否有值得记忆的信息，如果有，请输出1，否则输出0
|
||||
{text}
|
||||
请只输出1或0就好
|
||||
"""
|
||||
|
||||
try:
|
||||
response, _ = await self.summary_model.generate_response_async(prompt, temperature=0.5)
|
||||
if global_config.debug.show_prompt:
|
||||
print(prompt)
|
||||
print(response)
|
||||
|
||||
return "1" in response
|
||||
except Exception as e:
|
||||
logger.error(f"判断是否需要记忆出现错误：{str(e)} {traceback.format_exc()}")
|
||||
return False
|
||||
|
||||
async def build_memory(self, text):
|
||||
prompt = f"""
|
||||
以下内容中存在值得记忆的信息，请你从中总结出一段值得记忆的信息，并输出
|
||||
{text}
|
||||
请以json格式输出一段概括的记忆内容和关键词
|
||||
{{
|
||||
"memory_text": "记忆内容",
|
||||
"keywords": "关键词，用/划分"
|
||||
}}
|
||||
"""
|
||||
try:
|
||||
response, _ = await self.summary_model.generate_response_async(prompt, temperature=0.5)
|
||||
# print(prompt)
|
||||
# print(response)
|
||||
if not response:
|
||||
return None
|
||||
try:
|
||||
repaired = repair_json(response)
|
||||
result = json.loads(repaired)
|
||||
memory_text = result.get("memory_text", "")
|
||||
keywords = result.get("keywords", "")
|
||||
if isinstance(keywords, str):
|
||||
keywords_list = [k.strip() for k in keywords.split("/") if k.strip()]
|
||||
elif isinstance(keywords, list):
|
||||
keywords_list = keywords
|
||||
else:
|
||||
keywords_list = []
|
||||
return {"memory_text": memory_text, "keywords": keywords_list}
|
||||
except Exception as parse_e:
|
||||
logger.error(f"解析记忆json失败：{str(parse_e)} {traceback.format_exc()}")
|
||||
return None
|
||||
except Exception as e:
|
||||
logger.error(f"构建记忆出现错误：{str(e)} {traceback.format_exc()}")
|
||||
return None
|
||||
|
||||
async def create_and_store_memory(self, text: str):
|
||||
if_need = await self.if_need_build(text)
|
||||
if if_need:
|
||||
logger.info(f"需要记忆：{text}")
|
||||
memory = await self.build_memory(text)
|
||||
if memory and memory.get("memory_text"):
|
||||
memory_id = f"{self.chat_id}_{time.time()}"
|
||||
memory_item = MemoryItem(
|
||||
memory_id=memory_id,
|
||||
chat_id=self.chat_id,
|
||||
memory_text=memory["memory_text"],
|
||||
keywords=memory.get("keywords", []),
|
||||
)
|
||||
await self.store_memory(memory_item)
|
||||
else:
|
||||
logger.info(f"不需要记忆：{text}")
|
||||
|
||||
async def store_memory(self, memory_item: MemoryItem):
|
||||
memory = Memory(
|
||||
memory_id=memory_item.memory_id,
|
||||
chat_id=memory_item.chat_id,
|
||||
memory_text=memory_item.memory_text,
|
||||
keywords=memory_item.keywords,
|
||||
create_time=memory_item.create_time,
|
||||
last_view_time=memory_item.last_view_time,
|
||||
)
|
||||
memory.save()
|
||||
|
||||
async def get_memory(self, target: str):
|
||||
from json_repair import repair_json
|
||||
|
||||
prompt = f"""
|
||||
请根据以下发言内容，判断是否需要提取记忆
|
||||
{target}
|
||||
请用json格式输出，包含以下字段：
|
||||
其中，time的要求是：
|
||||
可以选择具体日期时间，格式为YYYY-MM-DD HH:MM:SS，或者大致时间，格式为YYYY-MM-DD
|
||||
可以选择相对时间，例如：今天，昨天，前天，5天前，1个月前
|
||||
可以选择留空进行模糊搜索
|
||||
{{
|
||||
"need_memory": 1,
|
||||
"keywords": "希望获取的记忆关键词，用/划分",
|
||||
"time": "希望获取的记忆大致时间"
|
||||
}}
|
||||
请只输出json格式，不要输出其他多余内容
|
||||
"""
|
||||
try:
|
||||
response, _ = await self.summary_model.generate_response_async(prompt, temperature=0.5)
|
||||
if global_config.debug.show_prompt:
|
||||
print(prompt)
|
||||
print(response)
|
||||
if not response:
|
||||
return None
|
||||
try:
|
||||
repaired = repair_json(response)
|
||||
result = json.loads(repaired)
|
||||
# 解析keywords
|
||||
keywords = result.get("keywords", "")
|
||||
if isinstance(keywords, str):
|
||||
keywords_list = [k.strip() for k in keywords.split("/") if k.strip()]
|
||||
elif isinstance(keywords, list):
|
||||
keywords_list = keywords
|
||||
else:
|
||||
keywords_list = []
|
||||
# 解析time为时间段
|
||||
time_str = result.get("time", "").strip()
|
||||
start_time, end_time = self._parse_time_range(time_str)
|
||||
logger.info(f"start_time: {start_time}, end_time: {end_time}")
|
||||
# 检索包含关键词的记忆
|
||||
memories_set = set()
|
||||
if start_time and end_time:
|
||||
start_ts = start_time.timestamp()
|
||||
end_ts = end_time.timestamp()
|
||||
query = Memory.select().where(
|
||||
(Memory.chat_id == self.chat_id)
|
||||
& (Memory.create_time >= start_ts) # type: ignore
|
||||
& (Memory.create_time < end_ts) # type: ignore
|
||||
)
|
||||
else:
|
||||
query = Memory.select().where(Memory.chat_id == self.chat_id)
|
||||
|
||||
for mem in query:
|
||||
# 对每条记忆
|
||||
mem_keywords = mem.keywords or ""
|
||||
parsed = ast.literal_eval(mem_keywords)
|
||||
if isinstance(parsed, list):
|
||||
mem_keywords = [str(k).strip() for k in parsed if str(k).strip()]
|
||||
else:
|
||||
mem_keywords = []
|
||||
# logger.info(f"mem_keywords: {mem_keywords}")
|
||||
# logger.info(f"keywords_list: {keywords_list}")
|
||||
for kw in keywords_list:
|
||||
# logger.info(f"kw: {kw}")
|
||||
# logger.info(f"kw in mem_keywords: {kw in mem_keywords}")
|
||||
if kw in mem_keywords:
|
||||
# logger.info(f"mem.memory_text: {mem.memory_text}")
|
||||
memories_set.add(mem.memory_text)
|
||||
break
|
||||
return list(memories_set)
|
||||
except Exception as parse_e:
|
||||
logger.error(f"解析记忆json失败：{str(parse_e)} {traceback.format_exc()}")
|
||||
return None
|
||||
except Exception as e:
|
||||
logger.error(f"获取记忆出现错误：{str(e)} {traceback.format_exc()}")
|
||||
return None
|
||||
|
||||
def _parse_time_range(self, time_str):
|
||||
# sourcery skip: extract-duplicate-method, use-contextlib-suppress
|
||||
"""
|
||||
支持解析如下格式：
|
||||
- 具体日期时间：YYYY-MM-DD HH:MM:SS
|
||||
- 具体日期：YYYY-MM-DD
|
||||
- 相对时间：今天，昨天，前天，N天前，N个月前
|
||||
- 空字符串：返回(None, None)
|
||||
"""
|
||||
now = datetime.now()
|
||||
if not time_str:
|
||||
return 0, now
|
||||
time_str = time_str.strip()
|
||||
# 具体日期时间
|
||||
try:
|
||||
dt = datetime.strptime(time_str, "%Y-%m-%d %H:%M:%S")
|
||||
return dt, dt + timedelta(hours=1)
|
||||
except Exception:
|
||||
pass
|
||||
# 具体日期
|
||||
try:
|
||||
dt = datetime.strptime(time_str, "%Y-%m-%d")
|
||||
return dt, dt + timedelta(days=1)
|
||||
except Exception:
|
||||
pass
|
||||
# 相对时间
|
||||
if time_str == "今天":
|
||||
start = now.replace(hour=0, minute=0, second=0, microsecond=0)
|
||||
end = start + timedelta(days=1)
|
||||
return start, end
|
||||
if time_str == "昨天":
|
||||
start = (now - timedelta(days=1)).replace(hour=0, minute=0, second=0, microsecond=0)
|
||||
end = start + timedelta(days=1)
|
||||
return start, end
|
||||
if time_str == "前天":
|
||||
start = (now - timedelta(days=2)).replace(hour=0, minute=0, second=0, microsecond=0)
|
||||
end = start + timedelta(days=1)
|
||||
return start, end
|
||||
if m := re.match(r"(\d+)天前", time_str):
|
||||
days = int(m.group(1))
|
||||
start = (now - timedelta(days=days)).replace(hour=0, minute=0, second=0, microsecond=0)
|
||||
end = start + timedelta(days=1)
|
||||
return start, end
|
||||
if m := re.match(r"(\d+)个月前", time_str):
|
||||
months = int(m.group(1))
|
||||
# 近似每月30天
|
||||
start = (now - timedelta(days=months * 30)).replace(hour=0, minute=0, second=0, microsecond=0)
|
||||
end = start + timedelta(days=1)
|
||||
return start, end
|
||||
# 其他无法解析
|
||||
return 0, now
|
||||
|
|
@ -26,7 +26,6 @@ from src.chat.utils.chat_message_builder import (
|
|||
)
|
||||
from src.chat.express.expression_selector import expression_selector
|
||||
from src.chat.memory_system.memory_activator import MemoryActivator
|
||||
from src.chat.memory_system.instant_memory import InstantMemory
|
||||
from src.mood.mood_manager import mood_manager
|
||||
from src.person_info.person_info import Person, is_person_known
|
||||
from src.plugin_system.base.component_types import ActionInfo, EventType
|
||||
|
|
@ -147,7 +146,6 @@ class DefaultReplyer:
|
|||
self.is_group_chat, self.chat_target_info = get_chat_type_and_target_info(self.chat_stream.stream_id)
|
||||
self.heart_fc_sender = HeartFCSender()
|
||||
self.memory_activator = MemoryActivator()
|
||||
self.instant_memory = InstantMemory(chat_id=self.chat_stream.stream_id)
|
||||
|
||||
from src.plugin_system.core.tool_use import ToolExecutor # 延迟导入ToolExecutor，不然会循环依赖
|
||||
|
||||
|
|
@ -375,20 +373,11 @@ class DefaultReplyer:
|
|||
|
||||
instant_memory = None
|
||||
|
||||
# running_memories = await self.memory_activator.activate_memory_with_chat_history(
|
||||
# target_message=target, chat_history=chat_history
|
||||
# )
|
||||
running_memories = await self.memory_activator.activate_memory_with_chat_history(
|
||||
target_message=target, chat_history=chat_history
|
||||
)
|
||||
running_memories = None
|
||||
|
||||
if global_config.memory.enable_instant_memory:
|
||||
chat_history_str = build_readable_messages(
|
||||
messages=chat_history, replace_bot_name=True, timestamp_mode="normal"
|
||||
)
|
||||
asyncio.create_task(self.instant_memory.create_and_store_memory(chat_history_str))
|
||||
|
||||
instant_memory = await self.instant_memory.get_memory(target)
|
||||
logger.info(f"即时记忆：{instant_memory}")
|
||||
|
||||
if not running_memories:
|
||||
return ""
|
||||
|
||||
|
|
|
|||
|
|
@ -834,3 +834,79 @@ def parse_keywords_string(keywords_input) -> list[str]:
|
|||
|
||||
# 如果没有分隔符，返回单个关键词
|
||||
return [keywords_str] if keywords_str else []
|
||||
|
||||
|
||||
|
||||
|
||||
def cut_key_words(concept_name: str) -> list[str]:
|
||||
"""对概念名称进行jieba分词，并过滤掉关键词列表中的关键词"""
|
||||
concept_name_tokens = list(jieba.cut(concept_name))
|
||||
|
||||
# 定义常见连词、停用词与标点
|
||||
conjunctions = {
|
||||
"和", "与", "及", "跟", "以及", "并且", "而且", "或", "或者", "并"
|
||||
}
|
||||
stop_words = {
|
||||
"的", "了", "呢", "吗", "吧", "啊", "哦", "恩", "嗯", "呀", "嘛", "哇",
|
||||
"在", "是", "很", "也", "又", "就", "都", "还", "更", "最", "被", "把",
|
||||
"给", "对", "和", "与", "及", "跟", "并", "而且", "或者", "或", "以及"
|
||||
}
|
||||
chinese_punctuations = set("，。！？、；：（）【】《》“”‘’—…·-——,.!?;:()[]<>'\"/\\")
|
||||
|
||||
# 清理空白并初步过滤纯标点
|
||||
cleaned_tokens = []
|
||||
for tok in concept_name_tokens:
|
||||
t = tok.strip()
|
||||
if not t:
|
||||
continue
|
||||
# 去除纯标点
|
||||
if all(ch in chinese_punctuations for ch in t):
|
||||
continue
|
||||
cleaned_tokens.append(t)
|
||||
|
||||
# 合并连词两侧的词（仅当两侧都存在且不是标点/停用词时）
|
||||
merged_tokens = []
|
||||
i = 0
|
||||
n = len(cleaned_tokens)
|
||||
while i < n:
|
||||
tok = cleaned_tokens[i]
|
||||
if tok in conjunctions and merged_tokens and i + 1 < n:
|
||||
left = merged_tokens[-1]
|
||||
right = cleaned_tokens[i + 1]
|
||||
# 左右都需要是有效词
|
||||
if left and right \
|
||||
and left not in conjunctions and right not in conjunctions \
|
||||
and left not in stop_words and right not in stop_words \
|
||||
and not all(ch in chinese_punctuations for ch in left) \
|
||||
and not all(ch in chinese_punctuations for ch in right):
|
||||
# 合并为一个新词，并替换掉左侧与跳过右侧
|
||||
combined = f"{left}{tok}{right}"
|
||||
merged_tokens[-1] = combined
|
||||
i += 2
|
||||
continue
|
||||
# 常规推进
|
||||
merged_tokens.append(tok)
|
||||
i += 1
|
||||
|
||||
# 二次过滤：去除停用词、单字符纯标点与无意义项
|
||||
result_tokens = []
|
||||
seen = set()
|
||||
# ban_words = set(getattr(global_config.memory, "memory_ban_words", []) or [])
|
||||
for tok in merged_tokens:
|
||||
if tok in conjunctions:
|
||||
# 独立连词丢弃
|
||||
continue
|
||||
if tok in stop_words:
|
||||
continue
|
||||
# if tok in ban_words:
|
||||
# continue
|
||||
if all(ch in chinese_punctuations for ch in tok):
|
||||
continue
|
||||
if tok.strip() == "":
|
||||
continue
|
||||
if tok not in seen:
|
||||
seen.add(tok)
|
||||
result_tokens.append(tok)
|
||||
|
||||
filtered_concept_name_tokens = result_tokens
|
||||
return filtered_concept_name_tokens
|
||||
|
|
@ -298,19 +298,6 @@ class GroupInfo(BaseModel):
|
|||
# database = db # 继承自 BaseModel
|
||||
table_name = "group_info"
|
||||
|
||||
|
||||
class Memory(BaseModel):
|
||||
memory_id = TextField(index=True)
|
||||
chat_id = TextField(null=True)
|
||||
memory_text = TextField(null=True)
|
||||
keywords = TextField(null=True)
|
||||
create_time = FloatField(null=True)
|
||||
last_view_time = FloatField(null=True)
|
||||
|
||||
class Meta:
|
||||
table_name = "memory"
|
||||
|
||||
|
||||
class Expression(BaseModel):
|
||||
"""
|
||||
用于存储表达风格的模型。
|
||||
|
|
@ -377,7 +364,6 @@ def create_tables():
|
|||
Expression,
|
||||
GraphNodes, # 添加图节点表
|
||||
GraphEdges, # 添加图边表
|
||||
Memory,
|
||||
ActionRecords, # 添加 ActionRecords 到初始化列表
|
||||
]
|
||||
)
|
||||
|
|
@ -403,7 +389,6 @@ def initialize_database(sync_constraints=False):
|
|||
OnlineTime,
|
||||
PersonInfo,
|
||||
Expression,
|
||||
Memory,
|
||||
GraphNodes,
|
||||
GraphEdges,
|
||||
ActionRecords, # 添加 ActionRecords 到初始化列表
|
||||
|
|
@ -501,7 +486,6 @@ def sync_field_constraints():
|
|||
OnlineTime,
|
||||
PersonInfo,
|
||||
Expression,
|
||||
Memory,
|
||||
GraphNodes,
|
||||
GraphEdges,
|
||||
ActionRecords,
|
||||
|
|
@ -680,7 +664,6 @@ def check_field_constraints():
|
|||
OnlineTime,
|
||||
PersonInfo,
|
||||
Expression,
|
||||
Memory,
|
||||
GraphNodes,
|
||||
GraphEdges,
|
||||
ActionRecords,
|
||||
|
|
|
|||
|
|
@ -60,9 +60,6 @@ class RelationshipConfig(ConfigBase):
|
|||
enable_relationship: bool = True
|
||||
"""是否启用关系系统"""
|
||||
|
||||
relation_frequency: int = 1
|
||||
"""关系频率，麦麦构建关系的速度"""
|
||||
|
||||
|
||||
@dataclass
|
||||
class ChatConfig(ConfigBase):
|
||||
|
|
@ -336,14 +333,8 @@ class MemoryConfig(ConfigBase):
|
|||
|
||||
enable_memory: bool = True
|
||||
"""是否启用记忆系统"""
|
||||
|
||||
memory_build_frequency: int = 1
|
||||
"""记忆构建频率（秒）"""
|
||||
|
||||
memory_compress_rate: float = 0.1
|
||||
"""记忆压缩率"""
|
||||
|
||||
forget_memory_interval: int = 1000
|
||||
forget_memory_interval: int = 1500
|
||||
"""记忆遗忘间隔（秒）"""
|
||||
|
||||
memory_forget_time: int = 24
|
||||
|
|
@ -355,9 +346,6 @@ class MemoryConfig(ConfigBase):
|
|||
memory_ban_words: list[str] = field(default_factory=lambda: ["表情包", "图片", "回复", "聊天记录"])
|
||||
"""不允许记忆的词列表"""
|
||||
|
||||
enable_instant_memory: bool = True
|
||||
"""是否启用即时记忆"""
|
||||
|
||||
|
||||
@dataclass
|
||||
class MoodConfig(ConfigBase):
|
||||
|
|
|
|||
|
|
@ -0,0 +1,34 @@
|
|||
{
|
||||
"manifest_version": 1,
|
||||
"name": "Memory Build组件",
|
||||
"version": "1.0.0",
|
||||
"description": "可以构建和管理记忆",
|
||||
"author": {
|
||||
"name": "Mai",
|
||||
"url": "https://github.com/MaiM-with-u"
|
||||
},
|
||||
"license": "GPL-v3.0-or-later",
|
||||
|
||||
"host_application": {
|
||||
"min_version": "0.10.1"
|
||||
},
|
||||
"homepage_url": "https://github.com/MaiM-with-u/maibot",
|
||||
"repository_url": "https://github.com/MaiM-with-u/maibot",
|
||||
"keywords": ["memory", "build", "built-in"],
|
||||
"categories": ["Memory"],
|
||||
|
||||
"default_locale": "zh-CN",
|
||||
"locales_path": "_locales",
|
||||
|
||||
"plugin_info": {
|
||||
"is_built_in": true,
|
||||
"plugin_type": "action_provider",
|
||||
"components": [
|
||||
{
|
||||
"type": "build_memory",
|
||||
"name": "build_memory",
|
||||
"description": "构建记忆"
|
||||
}
|
||||
]
|
||||
}
|
||||
}
|
||||
|
|
@ -0,0 +1,134 @@
|
|||
from typing import Tuple
|
||||
|
||||
from src.common.logger import get_logger
|
||||
from src.config.config import global_config
|
||||
from src.chat.utils.prompt_builder import Prompt
|
||||
from src.plugin_system import BaseAction, ActionActivationType
|
||||
from src.chat.memory_system.Hippocampus import hippocampus_manager
|
||||
from src.chat.utils.utils import cut_key_words
|
||||
|
||||
logger = get_logger("memory")
|
||||
|
||||
|
||||
def init_prompt():
|
||||
Prompt(
|
||||
"""
|
||||
以下是一些记忆条目的分类：
|
||||
----------------------
|
||||
{category_list}
|
||||
----------------------
|
||||
每一个分类条目类型代表了你对用户："{person_name}"的印象的一个类别
|
||||
|
||||
现在，你有一条对 {person_name} 的新记忆内容：
|
||||
{memory_point}
|
||||
|
||||
请判断该记忆内容是否属于上述分类，请给出分类的名称。
|
||||
如果不属于上述分类，请输出一个合适的分类名称，对新记忆内容进行概括。要求分类名具有概括性。
|
||||
注意分类数一般不超过5个
|
||||
请严格用json格式输出，不要输出任何其他内容：
|
||||
{{
|
||||
"category": "分类名称"
|
||||
}} """,
|
||||
"relation_category",
|
||||
)
|
||||
|
||||
Prompt(
|
||||
"""
|
||||
以下是有关{category}的现有记忆：
|
||||
----------------------
|
||||
{memory_list}
|
||||
----------------------
|
||||
|
||||
现在，你有一条对 {person_name} 的新记忆内容：
|
||||
{memory_point}
|
||||
|
||||
请判断该新记忆内容是否已经存在于现有记忆中，你可以对现有进行进行以下修改：
|
||||
注意，一般来说记忆内容不超过5个，且记忆文本不应太长
|
||||
|
||||
1.新增：当记忆内容不存在于现有记忆，且不存在矛盾，请用json格式输出：
|
||||
{{
|
||||
"new_memory": "需要新增的记忆内容"
|
||||
}}
|
||||
2.加深印象：如果这个新记忆已经存在于现有记忆中，在内容上与现有记忆类似，请用json格式输出：
|
||||
{{
|
||||
"memory_id": 1, #请输出你认为需要加深印象的，与新记忆内容类似的，已经存在的记忆的序号
|
||||
"integrate_memory": "加深后的记忆内容，合并内容类似的新记忆和旧记忆"
|
||||
}}
|
||||
3.整合：如果这个新记忆与现有记忆产生矛盾，请你结合其他记忆进行整合，用json格式输出：
|
||||
{{
|
||||
"memory_id": 1, #请输出你认为需要整合的，与新记忆存在矛盾的，已经存在的记忆的序号
|
||||
"integrate_memory": "整合后的记忆内容，合并内容矛盾的新记忆和旧记忆"
|
||||
}}
|
||||
|
||||
现在，请你根据情况选出合适的修改方式，并输出json，不要输出其他内容：
|
||||
""",
|
||||
"relation_category_update",
|
||||
)
|
||||
|
||||
|
||||
class BuildMemoryAction(BaseAction):
|
||||
"""关系动作 - 构建关系"""
|
||||
|
||||
activation_type = ActionActivationType.LLM_JUDGE
|
||||
parallel_action = True
|
||||
|
||||
# 动作基本信息
|
||||
action_name = "build_memory"
|
||||
action_description = "了解对于某个概念或者某件事的记忆，并存储下来，在之后的聊天中，你可以根据这条记忆来获取相关信息"
|
||||
|
||||
# 动作参数定义
|
||||
action_parameters = {
|
||||
"concept_name": "需要了解或记忆的概念或事件的名称",
|
||||
"concept_description": "需要了解或记忆的概念或事件的描述，需要具体且明确",
|
||||
}
|
||||
|
||||
# 动作使用场景
|
||||
action_require = [
|
||||
"了解对于某个概念或者某件事的记忆，并存储下来，在之后的聊天中，你可以根据这条记忆来获取相关信息",
|
||||
"有你不了解的概念",
|
||||
"有人要求你记住某个概念或者事件",
|
||||
"你对某件事或概念有新的理解，或产生了兴趣",
|
||||
]
|
||||
|
||||
# 关联类型
|
||||
associated_types = ["text"]
|
||||
|
||||
async def execute(self) -> Tuple[bool, str]:
|
||||
"""执行关系动作"""
|
||||
|
||||
try:
|
||||
# 1. 获取构建关系的原因
|
||||
concept_description = self.action_data.get("concept_description", "")
|
||||
logger.info(f"{self.log_prefix} 添加记忆原因: {self.reasoning}")
|
||||
concept_name = self.action_data.get("concept_name", "")
|
||||
# 2. 获取目标用户信息
|
||||
|
||||
|
||||
|
||||
# 对 concept_name 进行jieba分词
|
||||
concept_name_tokens = cut_key_words(concept_name)
|
||||
# logger.info(f"{self.log_prefix} 对 concept_name 进行分词结果: {concept_name_tokens}")
|
||||
|
||||
filtered_concept_name_tokens = [
|
||||
token for token in concept_name_tokens if all(keyword not in token for keyword in global_config.memory.memory_ban_words)
|
||||
]
|
||||
|
||||
if not filtered_concept_name_tokens:
|
||||
logger.warning(f"{self.log_prefix} 过滤后的概念名称列表为空，跳过添加记忆")
|
||||
return False, "过滤后的概念名称列表为空，跳过添加记忆"
|
||||
|
||||
similar_topics_dict = hippocampus_manager.get_hippocampus().parahippocampal_gyrus.get_similar_topics_from_keywords(filtered_concept_name_tokens)
|
||||
await hippocampus_manager.get_hippocampus().parahippocampal_gyrus.add_memory_with_similar(concept_description, similar_topics_dict)
|
||||
|
||||
|
||||
|
||||
return True, f"成功添加记忆: {concept_name}"
|
||||
|
||||
except Exception as e:
|
||||
logger.error(f"{self.log_prefix} 构建记忆时出错: {e}")
|
||||
return False, f"构建记忆时出错: {e}"
|
||||
|
||||
|
||||
|
||||
# 还缺一个关系的太多遗忘和对应的提取
|
||||
init_prompt()
|
||||
|
|
@ -0,0 +1,58 @@
|
|||
from typing import List, Tuple, Type
|
||||
|
||||
# 导入新插件系统
|
||||
from src.plugin_system import BasePlugin, register_plugin, ComponentInfo
|
||||
from src.plugin_system.base.config_types import ConfigField
|
||||
|
||||
# 导入依赖的系统组件
|
||||
from src.common.logger import get_logger
|
||||
|
||||
from src.plugins.built_in.memory.build_memory import BuildMemoryAction
|
||||
|
||||
logger = get_logger("relation_actions")
|
||||
|
||||
|
||||
@register_plugin
|
||||
class MemoryBuildPlugin(BasePlugin):
|
||||
"""关系动作插件
|
||||
|
||||
系统内置插件，提供基础的聊天交互功能：
|
||||
- Reply: 回复动作
|
||||
- NoReply: 不回复动作
|
||||
- Emoji: 表情动作
|
||||
|
||||
注意：插件基本信息优先从_manifest.json文件中读取
|
||||
"""
|
||||
|
||||
# 插件基本信息
|
||||
plugin_name: str = "memory_build" # 内部标识符
|
||||
enable_plugin: bool = True
|
||||
dependencies: list[str] = [] # 插件依赖列表
|
||||
python_dependencies: list[str] = [] # Python包依赖列表
|
||||
config_file_name: str = "config.toml"
|
||||
|
||||
# 配置节描述
|
||||
config_section_descriptions = {
|
||||
"plugin": "插件启用配置",
|
||||
"components": "核心组件启用配置",
|
||||
}
|
||||
|
||||
# 配置Schema定义
|
||||
config_schema: dict = {
|
||||
"plugin": {
|
||||
"enabled": ConfigField(type=bool, default=True, description="是否启用插件"),
|
||||
"config_version": ConfigField(type=str, default="1.1.0", description="配置文件版本"),
|
||||
},
|
||||
"components": {
|
||||
"memory_max_memory_num": ConfigField(type=int, default=10, description="记忆最大数量"),
|
||||
},
|
||||
}
|
||||
|
||||
def get_plugin_components(self) -> List[Tuple[ComponentInfo, Type]]:
|
||||
"""返回插件包含的组件列表"""
|
||||
|
||||
# --- 根据配置注册组件 ---
|
||||
components = []
|
||||
components.append((BuildMemoryAction.get_action_info(), BuildMemoryAction))
|
||||
|
||||
return components
|
||||
|
|
@ -9,7 +9,7 @@
|
|||
},
|
||||
"license": "GPL-v3.0-or-later",
|
||||
"host_application": {
|
||||
"min_version": "0.9.1"
|
||||
"min_version": "0.10.1"
|
||||
},
|
||||
"homepage_url": "https://github.com/MaiM-with-u/maibot",
|
||||
"repository_url": "https://github.com/MaiM-with-u/maibot",
|
||||
|
|
|
|||
|
|
@ -1,6 +1,7 @@
|
|||
import json
|
||||
from json_repair import repair_json
|
||||
from typing import Tuple
|
||||
import time
|
||||
|
||||
from src.common.logger import get_logger
|
||||
from src.config.config import global_config
|
||||
|
|
@ -79,16 +80,6 @@ class BuildRelationAction(BaseAction):
|
|||
action_name = "build_relation"
|
||||
action_description = "了解对于某人的记忆，并添加到你对对方的印象中"
|
||||
|
||||
# LLM判断提示词
|
||||
llm_judge_prompt = """
|
||||
判定是否需要使用关系动作，添加对于某人的记忆：
|
||||
1. 对方与你的交互让你对其有新记忆
|
||||
2. 对方有提到其个人信息，包括喜好，身份，等等
|
||||
3. 对方希望你记住对方的信息
|
||||
|
||||
请回答"是"或"否"。
|
||||
"""
|
||||
|
||||
# 动作参数定义
|
||||
action_parameters = {"person_name": "需要了解或记忆的人的名称", "impression": "需要了解的对某人的记忆或印象"}
|
||||
|
||||
|
|
@ -109,13 +100,17 @@ class BuildRelationAction(BaseAction):
|
|||
try:
|
||||
# 1. 获取构建关系的原因
|
||||
impression = self.action_data.get("impression", "")
|
||||
logger.info(f"{self.log_prefix} 添加记忆原因: {self.reasoning}")
|
||||
logger.info(f"{self.log_prefix} 添加关系印象原因: {self.reasoning}")
|
||||
person_name = self.action_data.get("person_name", "")
|
||||
# 2. 获取目标用户信息
|
||||
person = Person(person_name=person_name)
|
||||
if not person.is_known:
|
||||
logger.warning(f"{self.log_prefix} 用户 {person_name} 不存在，跳过添加记忆")
|
||||
return False, f"用户 {person_name} 不存在，跳过添加记忆"
|
||||
|
||||
person.last_know = time.time()
|
||||
person.know_times += 1
|
||||
person.sync_to_database()
|
||||
|
||||
category_list = person.get_all_category()
|
||||
if not category_list:
|
||||
|
|
@ -195,6 +190,8 @@ class BuildRelationAction(BaseAction):
|
|||
# 新记忆
|
||||
person.memory_points.append(f"{category}:{new_memory}:1.0")
|
||||
person.sync_to_database()
|
||||
|
||||
logger.info(f"{self.log_prefix} 为{person.person_name}新增记忆点: {new_memory}")
|
||||
|
||||
return True, f"为{person.person_name}新增记忆点: {new_memory}"
|
||||
elif memory_id and integrate_memory:
|
||||
|
|
@ -204,12 +201,14 @@ class BuildRelationAction(BaseAction):
|
|||
del_count = person.del_memory(category, memory_content)
|
||||
|
||||
if del_count > 0:
|
||||
logger.info(f"{self.log_prefix} 删除记忆点: {memory_content}")
|
||||
# logger.info(f"{self.log_prefix} 删除记忆点: {memory_content}")
|
||||
|
||||
memory_weight = get_weight_from_memory(memory)
|
||||
person.memory_points.append(f"{category}:{integrate_memory}:{memory_weight + 1.0}")
|
||||
person.sync_to_database()
|
||||
|
||||
logger.info(f"{self.log_prefix} 更新{person.person_name}的记忆点: {memory_content} -> {integrate_memory}")
|
||||
|
||||
return True, f"更新{person.person_name}的记忆点: {memory_content} -> {integrate_memory}"
|
||||
|
||||
else:
|
||||
|
|
|
|||
|
|
@ -1,5 +1,5 @@
|
|||
[inner]
|
||||
version = "6.7.1"
|
||||
version = "6.7.2"
|
||||
|
||||
#----以下是给开发人员阅读的，如果你只是部署了麦麦，不需要阅读----
|
||||
#如果你想要修改配置文件，请递增version的值
|
||||
|
|
@ -65,8 +65,6 @@ focus_value = 0.5
|
|||
|
||||
max_context_size = 20 # 上下文长度
|
||||
|
||||
interest_rate_mode = "fast" #激活值计算模式，可选fast或者accurate
|
||||
|
||||
planner_size = 2.5 # 副规划器大小，越小，麦麦的动作执行能力越精细，但是消耗更多token，调大可以缓解429类错误
|
||||
|
||||
mentioned_bot_inevitable_reply = true # 提及 bot 大概率回复
|
||||
|
|
@ -102,22 +100,8 @@ talk_frequency_adjust = [
|
|||
# - 后续元素是"时间,频率"格式，表示从该时间开始使用该活跃度，直到下一个时间点
|
||||
# - 优先级：特定聊天流配置 > 全局配置 > 默认 talk_frequency
|
||||
|
||||
|
||||
[relationship]
|
||||
enable_relationship = true # 是否启用关系系统
|
||||
relation_frequency = 1 # 关系频率，麦麦构建关系的频率
|
||||
|
||||
[message_receive]
|
||||
# 以下是消息过滤，可以根据规则过滤特定消息，将不会读取这些消息
|
||||
ban_words = [
|
||||
# "403","张三"
|
||||
]
|
||||
|
||||
ban_msgs_regex = [
|
||||
# 需要过滤的消息（原始消息）匹配的正则表达式，匹配到的消息将被过滤，若不了解正则表达式请勿修改
|
||||
#"https?://[^\\s]+", # 匹配https链接
|
||||
#"\\d{4}-\\d{2}-\\d{2}", # 匹配日期
|
||||
]
|
||||
|
||||
[tool]
|
||||
enable_tool = false # 是否在普通聊天中启用工具
|
||||
|
|
@ -138,21 +122,30 @@ filtration_prompt = "符合公序良俗" # 表情包过滤要求，只有符合
|
|||
|
||||
[memory]
|
||||
enable_memory = true # 是否启用记忆系统
|
||||
memory_build_frequency = 1 # 记忆构建频率 越高，麦麦学习越多
|
||||
memory_compress_rate = 0.1 # 记忆压缩率 控制记忆精简程度 建议保持默认,调高可以获得更多信息，但是冗余信息也会增多
|
||||
|
||||
forget_memory_interval = 3000 # 记忆遗忘间隔 单位秒 间隔越低，麦麦遗忘越频繁，记忆更精简，但更难学习
|
||||
forget_memory_interval = 1500 # 记忆遗忘间隔 单位秒 间隔越低，麦麦遗忘越频繁，记忆更精简，但更难学习
|
||||
memory_forget_time = 48 #多长时间后的记忆会被遗忘 单位小时
|
||||
memory_forget_percentage = 0.008 # 记忆遗忘比例 控制记忆遗忘程度 越大遗忘越多 建议保持默认
|
||||
|
||||
enable_instant_memory = false # 是否启用即时记忆，测试功能，可能存在未知问题
|
||||
|
||||
#不希望记忆的词，已经记忆的不会受到影响，需要手动清理
|
||||
memory_ban_words = [ "表情包", "图片", "回复", "聊天记录" ]
|
||||
|
||||
[voice]
|
||||
enable_asr = false # 是否启用语音识别，启用后麦麦可以识别语音消息，启用该功能需要配置语音识别模型[model.voice]s
|
||||
|
||||
[message_receive]
|
||||
# 以下是消息过滤，可以根据规则过滤特定消息，将不会读取这些消息
|
||||
ban_words = [
|
||||
# "403","张三"
|
||||
]
|
||||
|
||||
ban_msgs_regex = [
|
||||
# 需要过滤的消息（原始消息）匹配的正则表达式，匹配到的消息将被过滤，若不了解正则表达式请勿修改
|
||||
#"https?://[^\\s]+", # 匹配https链接
|
||||
#"\\d{4}-\\d{2}-\\d{2}", # 匹配日期
|
||||
]
|
||||
|
||||
|
||||
[lpmm_knowledge] # lpmm知识库配置
|
||||
enable = false # 是否启用lpmm知识库
|
||||
rag_synonym_search_top_k = 10 # 同义词搜索TopK
|
||||
|
|
|
|||
Loading…
Reference in New Issue